From 3176caa61e47f8957aa0c2662a3bb253f881acf1 Mon Sep 17 00:00:00 2001 From: TahminaMojumder Date: Fri, 6 Oct 2023 17:32:15 +0200 Subject: [PATCH 1/5] created createLikelihoodHelp --- .../inst/examples/createLikelihoodHelp.R | 29 +++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 BayesianTools/inst/examples/createLikelihoodHelp.R diff --git a/BayesianTools/inst/examples/createLikelihoodHelp.R b/BayesianTools/inst/examples/createLikelihoodHelp.R new file mode 100644 index 0000000..c6372d9 --- /dev/null +++ b/BayesianTools/inst/examples/createLikelihoodHelp.R @@ -0,0 +1,29 @@ + + +# likelihood function can be created with or without createBayesianSetup. ??createBayesianSetup provides additional properties. + + +data = rnorm(20) + +# create standardized likelihood function for Gaussian likelihood function +likelihood <- function(x) likelihoodIidNormal(rnorm(20, mean = x), data, 2) +likelihoodClass <- createLikelihood(likelihood) +bayesianSetup <- createBayesianSetup(likelihood = likelihoodClass, lower = -10, upper = 10) +settings = list(iterations = 100) +plotSensitivity(bayesianSetup) +out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) + + +# create likelihood function for AR1 likelihood function +likelihood <- function(x) likelihoodAR1(rnorm(20, mean = x), data, 2, 0.6) +bayesianSetup <- createBayesianSetup(likelihood = likelihood, lower = -10, upper = 10) +settings = list(iterations = 100) +out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) + + +# create likelihood function for banana likelihood function +likelihood <- testDensityBanana +bayesianSetup <- createBayesianSetup(likelihood = likelihood, lower = rep(-10,2), upper = rep(10,2)) +settings = list(iterations = 100) +out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) + From 1af93b7ccbf7ead8c87ef64f08557924c5f2e0fd Mon Sep 17 00:00:00 2001 From: TahminaMojumder Date: Wed, 18 Oct 2023 16:10:15 +0200 Subject: [PATCH 2/5] updated createLikelihoodHelp and created plotSensitivityHelp --- .../inst/examples/createLikelihoodHelp.R | 61 ++++++++++++++----- .../inst/examples/plotSensitivityHelp.R | 22 ++++++- 2 files changed, 67 insertions(+), 16 deletions(-) diff --git a/BayesianTools/inst/examples/createLikelihoodHelp.R b/BayesianTools/inst/examples/createLikelihoodHelp.R index c6372d9..d26f6f2 100644 --- a/BayesianTools/inst/examples/createLikelihoodHelp.R +++ b/BayesianTools/inst/examples/createLikelihoodHelp.R @@ -2,28 +2,61 @@ # likelihood function can be created with or without createBayesianSetup. ??createBayesianSetup provides additional properties. - data = rnorm(20) -# create standardized likelihood function for Gaussian likelihood function -likelihood <- function(x) likelihoodIidNormal(rnorm(20, mean = x), data, 2) +# create standardized likelihood density for Gaussian likelihood function +# fitting parameters mean and standard deviation + +likelihood <- function(x) { + predicted <- rep(x[1], length(data)) + LL = likelihoodIidNormal(predicted, data, x[2]) + return(LL) +} + +likelihood(x = c(1,1)) + +# The next step is optional, you can provide the likelihood density directly +# to BayesianSetup and createLikelihood will be called automatically. +# The purpose of the createLikelihood function is to parallelize the +# density function and to add some convenience functions, +# for example to catch exceptions that are raised by the call to likelihood likelihoodClass <- createLikelihood(likelihood) -bayesianSetup <- createBayesianSetup(likelihood = likelihoodClass, lower = -10, upper = 10) -settings = list(iterations = 100) -plotSensitivity(bayesianSetup) + +bayesianSetup <- createBayesianSetup(likelihood = likelihoodClass, lower = c(-10,0.001), upper = c(10, 5)) + +settings = list(iterations = 1000) out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) +summary(out) + + + +# simulate temporally autocorrelated data +AR1sim<-function(n, a){ + x = rep(NA, n) + x[1] = 0 + for(i in 2:n){ + x[i] = a * x[i-1] + (1-a) * rnorm(1) + } + return(x) +} + +set.seed(123) +data = AR1sim(1000, 0.5) +plot(data, type = "l") # create likelihood function for AR1 likelihood function -likelihood <- function(x) likelihoodAR1(rnorm(20, mean = x), data, 2, 0.6) -bayesianSetup <- createBayesianSetup(likelihood = likelihood, lower = -10, upper = 10) -settings = list(iterations = 100) -out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) +likelihood <- function(x) { + predicted <- rep(x[1], length(data)) + LL = likelihoodAR1(predicted, data, x[2], x[3]) + return(LL) +} +bayesianSetup <- createBayesianSetup(likelihood = likelihood, + lower = c(-10,0.001, 0), upper = c(10, 5, 0.99)) -# create likelihood function for banana likelihood function -likelihood <- testDensityBanana -bayesianSetup <- createBayesianSetup(likelihood = likelihood, lower = rep(-10,2), upper = rep(10,2)) -settings = list(iterations = 100) +settings = list(iterations = 1000) out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) +summary(out, start = 200) +plot(out, start = 200) diff --git a/BayesianTools/inst/examples/plotSensitivityHelp.R b/BayesianTools/inst/examples/plotSensitivityHelp.R index 7afbdb5..50efe31 100644 --- a/BayesianTools/inst/examples/plotSensitivityHelp.R +++ b/BayesianTools/inst/examples/plotSensitivityHelp.R @@ -1,5 +1,23 @@ -ll <- testDensityBanana -bayesianSetup <- createBayesianSetup(likelihood = ll, lower = rep(-10, 2), upper = rep(10, 2)) + +data = rnorm(20) + +# create standardized likelihood density for Gaussian likelihood function +# fitting parameters mean and standard deviation + +likelihood <- function(x) { + predicted <- rep(x[1], length(data)) + LL = likelihoodIidNormal(predicted, data, x[2]) + return(LL) +} + + +# showing posterior response surface for parameter 1 +# note that this is plotted at +bayesianSetup$prior$best +plotSensitivity(bayesianSetup, selection = 1) plotSensitivity(bayesianSetup) +plotSensitivity(bayesianSetup, equalScale = F) + + From 89a20d9a30d5a41c56bc202bbe10368a5277a80c Mon Sep 17 00:00:00 2001 From: TahminaMojumder Date: Wed, 25 Oct 2023 10:10:50 +0200 Subject: [PATCH 3/5] Added description and figure in help file for plot sensitivity --- BayesianTools/R/classLikelihood.R | 1 + BayesianTools/R/plotSensitivityOAT.R | 7 ++ .../inst/examples/createLikelihoodHelp.R | 9 +-- .../inst/examples/plotSensitivityHelp.R | 13 ++-- BayesianTools/man/createLikelihood.Rd | 65 ++++++++++++++++++ .../plotSensitivity-ScaleDontMatch.png | Bin 0 -> 13202 bytes .../figures/plotSensitivity-ScaleFalse.png | Bin 0 -> 17237 bytes .../figures/sensitivityPlotScaleDontMatch.png | Bin 0 -> 22189 bytes .../man/figures/sensitivityPlotScaleFalse.png | Bin 0 -> 31450 bytes BayesianTools/man/plotSensitivity.Rd | 30 +++++++- Development/Figures/figures.R | 21 ++++++ 11 files changed, 132 insertions(+), 14 deletions(-) create mode 100644 BayesianTools/man/figures/plotSensitivity-ScaleDontMatch.png create mode 100644 BayesianTools/man/figures/plotSensitivity-ScaleFalse.png create mode 100644 BayesianTools/man/figures/sensitivityPlotScaleDontMatch.png create mode 100644 BayesianTools/man/figures/sensitivityPlotScaleFalse.png create mode 100644 Development/Figures/figures.R diff --git a/BayesianTools/R/classLikelihood.R b/BayesianTools/R/classLikelihood.R index 905d9a5..887f5ac 100644 --- a/BayesianTools/R/classLikelihood.R +++ b/BayesianTools/R/classLikelihood.R @@ -8,6 +8,7 @@ #' @param sampler sampler #' @seealso \code{\link{likelihoodIidNormal}} \cr #' \code{\link{likelihoodAR1}} \cr +#' @example /inst/examples/createLikelihoodHelp.R #' @export createLikelihood <- function(likelihood, names = NULL, parallel = F, catchDuplicates=T, sampler = NULL, parallelOptions = NULL){ diff --git a/BayesianTools/R/plotSensitivityOAT.R b/BayesianTools/R/plotSensitivityOAT.R index 1c2b060..35e1b32 100644 --- a/BayesianTools/R/plotSensitivityOAT.R +++ b/BayesianTools/R/plotSensitivityOAT.R @@ -4,6 +4,13 @@ #' @param selection indices of selected parameters #' @param equalScale if T, y axis of all plots will have the same scale #' @note This function can also be used for sensitivity analysis of an arbitrary output - just create a BayesianSetup with this output. +#' @details +#' When the scale of the parameter do not match then the plot looks like this : +#' ![](plotSensitivity-ScaleDontMatch.png "Sensitivity Plot") +#' +#' In that case, we should put 'equalScale = F'. Then the plot looks like this: +#' ![](plotSensitivity-ScaleFalse.png "Sensitivity Plot") +#' #' @example /inst/examples/plotSensitivityHelp.R #' @export plotSensitivity <- function(bayesianSetup, selection = NULL, equalScale = T){ diff --git a/BayesianTools/inst/examples/createLikelihoodHelp.R b/BayesianTools/inst/examples/createLikelihoodHelp.R index d26f6f2..1ccd278 100644 --- a/BayesianTools/inst/examples/createLikelihoodHelp.R +++ b/BayesianTools/inst/examples/createLikelihoodHelp.R @@ -1,7 +1,5 @@ -# likelihood function can be created with or without createBayesianSetup. ??createBayesianSetup provides additional properties. - data = rnorm(20) # create standardized likelihood density for Gaussian likelihood function @@ -22,7 +20,9 @@ likelihood(x = c(1,1)) # for example to catch exceptions that are raised by the call to likelihood likelihoodClass <- createLikelihood(likelihood) -bayesianSetup <- createBayesianSetup(likelihood = likelihoodClass, lower = c(-10,0.001), upper = c(10, 5)) +bayesianSetup <- createBayesianSetup(likelihood = likelihoodClass, + lower = c(-10,0.001), + upper = c(10, 5)) settings = list(iterations = 1000) out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) @@ -53,7 +53,8 @@ likelihood <- function(x) { } bayesianSetup <- createBayesianSetup(likelihood = likelihood, - lower = c(-10,0.001, 0), upper = c(10, 5, 0.99)) + lower = c(-10,0.001, 0), + upper = c(10, 5, 0.99)) settings = list(iterations = 1000) out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) diff --git a/BayesianTools/inst/examples/plotSensitivityHelp.R b/BayesianTools/inst/examples/plotSensitivityHelp.R index 50efe31..6849937 100644 --- a/BayesianTools/inst/examples/plotSensitivityHelp.R +++ b/BayesianTools/inst/examples/plotSensitivityHelp.R @@ -1,23 +1,22 @@ - - data = rnorm(20) # create standardized likelihood density for Gaussian likelihood function # fitting parameters mean and standard deviation - likelihood <- function(x) { predicted <- rep(x[1], length(data)) LL = likelihoodIidNormal(predicted, data, x[2]) return(LL) } +setup = createBayesianSetup(likelihood, lower = c(-10, 0.01), upper = c(10,5)) # showing posterior response surface for parameter 1 # note that this is plotted at -bayesianSetup$prior$best -plotSensitivity(bayesianSetup, selection = 1) +setup$prior$best +plotSensitivity(setup, selection = 2) -plotSensitivity(bayesianSetup) -plotSensitivity(bayesianSetup, equalScale = F) +plotSensitivity(setup) +# when parameters are not on the same scale, we can use +plotSensitivity(setup, equalScale = F) diff --git a/BayesianTools/man/createLikelihood.Rd b/BayesianTools/man/createLikelihood.Rd index 9b9d33b..5db5de8 100644 --- a/BayesianTools/man/createLikelihood.Rd +++ b/BayesianTools/man/createLikelihood.Rd @@ -28,6 +28,71 @@ createLikelihood( } \description{ Creates a standardized likelihood class#' +} +\examples{ + + +data = rnorm(20) + +# create standardized likelihood density for Gaussian likelihood function +# fitting parameters mean and standard deviation + +likelihood <- function(x) { + predicted <- rep(x[1], length(data)) + LL = likelihoodIidNormal(predicted, data, x[2]) + return(LL) +} + +likelihood(x = c(1,1)) + +# The next step is optional, you can provide the likelihood density directly +# to BayesianSetup and createLikelihood will be called automatically. +# The purpose of the createLikelihood function is to parallelize the +# density function and to add some convenience functions, +# for example to catch exceptions that are raised by the call to likelihood +likelihoodClass <- createLikelihood(likelihood) + +bayesianSetup <- createBayesianSetup(likelihood = likelihoodClass, + lower = c(-10,0.001), + upper = c(10, 5)) + +settings = list(iterations = 1000) +out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) +summary(out) + + + +# simulate temporally autocorrelated data +AR1sim<-function(n, a){ + x = rep(NA, n) + x[1] = 0 + for(i in 2:n){ + x[i] = a * x[i-1] + (1-a) * rnorm(1) + } + return(x) +} + +set.seed(123) +data = AR1sim(1000, 0.5) +plot(data, type = "l") + + +# create likelihood function for AR1 likelihood function +likelihood <- function(x) { + predicted <- rep(x[1], length(data)) + LL = likelihoodAR1(predicted, data, x[2], x[3]) + return(LL) +} + +bayesianSetup <- createBayesianSetup(likelihood = likelihood, + lower = c(-10,0.001, 0), + upper = c(10, 5, 0.99)) + +settings = list(iterations = 1000) +out <- runMCMC(bayesianSetup, sampler = "DEzs", settings) +summary(out, start = 200) +plot(out, start = 200) + } \seealso{ \code{\link{likelihoodIidNormal}} \cr diff --git a/BayesianTools/man/figures/plotSensitivity-ScaleDontMatch.png b/BayesianTools/man/figures/plotSensitivity-ScaleDontMatch.png new file mode 100644 index 0000000000000000000000000000000000000000..fe8e9326a31ebb93d85979242067d1629f083f05 GIT binary patch literal 13202 zcmeHtg;&(yw>HcS!T?_w8k87XxRA*4%MkS+=7?v&O6q?8s=x+J6oL|RG^rKCX` 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zcKpOIBV&#VN2fjBO1|-z7KcWpJXCSap6e@Usv7}u(3L zKJc7{qMPcMlsh3m-qMET`PtH}Vg6kbYrxnWcabRUEdS*+C^akU=q1W;Lp#y~OiY(N zGS)(YmC90k!3}Xl76FT&Zqo~+z%QU1Y&k->yL!00-BF*kIQV4}Ntv>zKoArS-RZ%v znxfP2QB^3hm)@yU2m+79>SI={oO9)TDwl*#=GrBXu()-fi(SaR>m4HACMICPB8}c6&dS? zFmmy_T^@`8PX#47YqODGxu7^m*Z?avhq}BryvRIt_TbsmC&i9M-{uch=dwWxVvN#N zq5yU3SEi~tda&2x;kO3;`oo@+smun$!QDP4Q?v4qP)ZGE#w+u`8NQphuXlyvnV#3-pF0Klz#_Thv#`*Fi{(u873kxv3< zzzh54|GHybGtCVV!ccKoahU3uSEKo&2X9V;KzYCED%ZjNbL2wox?L$*aS&h|Xz=^Q Zo3{^dE=5hyBfnNwSzcZ4 Date: Wed, 25 Oct 2023 12:44:54 +0200 Subject: [PATCH 4/5] Run check --- BayesianTools/R/plotSensitivityOAT.R | 6 +++--- BayesianTools/inst/examples/plotSensitivityHelp.R | 2 +- BayesianTools/man/plotSensitivity.Rd | 6 +++--- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/BayesianTools/R/plotSensitivityOAT.R b/BayesianTools/R/plotSensitivityOAT.R index 35e1b32..d7731e7 100644 --- a/BayesianTools/R/plotSensitivityOAT.R +++ b/BayesianTools/R/plotSensitivityOAT.R @@ -2,7 +2,7 @@ #' @author Florian Hartig #' @param bayesianSetup An object of class BayesianSetup #' @param selection indices of selected parameters -#' @param equalScale if T, y axis of all plots will have the same scale +#' @param equalScale logical, if TRUE - y axis of all plots will have the same scale #' @note This function can also be used for sensitivity analysis of an arbitrary output - just create a BayesianSetup with this output. #' @details #' When the scale of the parameter do not match then the plot looks like this : @@ -13,7 +13,7 @@ #' #' @example /inst/examples/plotSensitivityHelp.R #' @export -plotSensitivity <- function(bayesianSetup, selection = NULL, equalScale = T){ +plotSensitivity <- function(bayesianSetup, selection = NULL, equalScale = TRUE){ if (is.null(selection)) selection = 1:bayesianSetup$numPars n = length(selection) @@ -46,7 +46,7 @@ plotSensitivity <- function(bayesianSetup, selection = NULL, equalScale = T){ for (i in 1:n){ - if(equalScale == T) plot(resp~par, xlab = names[i], type = "l", col = "red", data = post[[i]], ylim = c(minR, maxR), ylab = "Response") + if(equalScale == TRUE) plot(resp~par, xlab = names[i], type = "l", col = "red", data = post[[i]], ylim = c(minR, maxR), ylab = "Response") else plot(resp~par, xlab = names[i], type = "l", col = "red", data = post[[i]], ylab = "Response") abline(v = refPar[i]) diff --git a/BayesianTools/inst/examples/plotSensitivityHelp.R b/BayesianTools/inst/examples/plotSensitivityHelp.R index 6849937..1d2109c 100644 --- a/BayesianTools/inst/examples/plotSensitivityHelp.R +++ b/BayesianTools/inst/examples/plotSensitivityHelp.R @@ -17,6 +17,6 @@ plotSensitivity(setup, selection = 2) plotSensitivity(setup) # when parameters are not on the same scale, we can use -plotSensitivity(setup, equalScale = F) +plotSensitivity(setup, equalScale = FALSE) diff --git a/BayesianTools/man/plotSensitivity.Rd b/BayesianTools/man/plotSensitivity.Rd index dac665a..f320320 100644 --- a/BayesianTools/man/plotSensitivity.Rd +++ b/BayesianTools/man/plotSensitivity.Rd @@ -4,14 +4,14 @@ \alias{plotSensitivity} \title{Performs a one-factor-at-a-time sensitivity analysis for the posterior of a given bayesianSetup within the prior range.} \usage{ -plotSensitivity(bayesianSetup, selection = NULL, equalScale = T) +plotSensitivity(bayesianSetup, selection = NULL, equalScale = TRUE) } \arguments{ \item{bayesianSetup}{An object of class BayesianSetup} \item{selection}{indices of selected parameters} -\item{equalScale}{if T, y axis of all plots will have the same scale} +\item{equalScale}{logical, if TRUE - y axis of all plots will have the same scale} } \description{ Performs a one-factor-at-a-time sensitivity analysis for the posterior of a given bayesianSetup within the prior range. @@ -46,7 +46,7 @@ plotSensitivity(setup, selection = 2) plotSensitivity(setup) # when parameters are not on the same scale, we can use -plotSensitivity(setup, equalScale = F) +plotSensitivity(setup, equalScale = FALSE) } From 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